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基于视点差异和多分类器的三维模型分类

丁博 范宇飞 高源 何勇军

丁博, 范宇飞, 高源, 何勇军. 基于视点差异和多分类器的三维模型分类[J]. 电子与信息学报, 2022, 44(11): 3977-3986. doi: 10.11999/JEIT210823
引用本文: 丁博, 范宇飞, 高源, 何勇军. 基于视点差异和多分类器的三维模型分类[J]. 电子与信息学报, 2022, 44(11): 3977-3986. doi: 10.11999/JEIT210823
DING Bo, FAN Yufei, GAO Yuan, HE Yongjun. 3D Model Classification Based on Viewpoint Differences and Multiple Classifiers[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3977-3986. doi: 10.11999/JEIT210823
Citation: DING Bo, FAN Yufei, GAO Yuan, HE Yongjun. 3D Model Classification Based on Viewpoint Differences and Multiple Classifiers[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3977-3986. doi: 10.11999/JEIT210823

基于视点差异和多分类器的三维模型分类

doi: 10.11999/JEIT210823
基金项目: 国家自然科学基金(61673142),黑龙江省自然科学基金(JJ2019JQ0013)
详细信息
    作者简介:

    丁博:女,副教授,研究方向为计算机图形学、CAD和人工智能

    范宇飞:男,硕士生,研究方向为计算机图形学、CAD和人工智能

    高源:男,硕士生,研究方向为计算机图形学、CAD和人工智能

    何勇军:男,教授,研究方向为模式识别和人工智能

    通讯作者:

    何勇军 holy_wit@163.com

  • 中图分类号: TN911.73; TP315.69

3D Model Classification Based on Viewpoint Differences and Multiple Classifiers

Funds: The National Natural Science Foundation of China (61673142), The Natural Science Foundation of Heilongjiang Province of China (JJ2019JQ0013)
  • 摘要: 基于视图的3维模型分类方法与深度学习融合能有效提升模型分类的准确率。但目前的方法将相同类别的3维模型所有视点上的视图归为一类,忽略了不同视点上的视图差异,导致分类器很难学习到一个合理的分类面。为解决这一问题,该文提出一个基于深度神经网络的3维模型分类方法。该方法在3维模型的周围均匀设置多个视点组,为每个视点组训练1个视图分类器,充分挖掘不同视点组下的3维模型深度信息。这些分类器共享1个特征提取网络,但却有各自的分类网络。为了使提取的视图特征具有区分性,在特征提取网络中加入注意力机制;为了对非本视点组的视图建模,在分类网络中增加了附加类。在分类阶段首先提出一个视图选择策略,从大量视图中选择少量视图用于分类,以提高分类效率。然后提出一个分类策略通过分类视图实现可靠的3维模型分类。在ModelNet10和ModelNet40上的实验结果表明,该方法在仅用3张视图的情况下分类准确率高达93.6%和91.0%。
  • 图  1  不同视点组下的视图

    图  2  分类过程

    图  3  视点组设置

    图  4  视点设置俯视图

    图  5  加入CBAM的特征提取网络

    图  6  分类器的训练过程

    图  7  视点组选择

    图  8  基于3张视图的3维模型分类混淆矩阵

    图  9  不同视图数量的分类准确率

    图  10  不同类别中3维模型分类平均耗时

    表  1  ModelNet10数据集

    分类器类型测试集训练集
    前10类前10类平均视图数第11类前10类前10类平均视图数第11类
    视图分类器8172817363235919359215964
    基线系统49032490321551421551
    下载: 导出CSV

    表  2  视图分类准确率(%)

    CBAM附加类数据集视点组1(上)视点组2(左)视点组3(前)视点组4(右)视点组5(后)视点组6(下)
    训练94.9595.7196.2895.4995.9594.62
    测试85.8887.2189.7286.7389.0383.92
    训练95.0394.1495.5393.2195.8495.16
    测试89.6187.2589.7487.0388.9188.41
    训练94.9595.8696.9195.9195.1393.93
    测试89.1889.3790.8289.3588.6786.94
    训练94.9992.9493.1792.9991.4994.64
    测试91.5290.4190.9390.0289.1990.38
    下载: 导出CSV

    表  3  分类准确率比较(%)

    方法视图数准确率
    ModelNet10ModelNet40
    DeepPano[13]185.577.6
    Geometry image[14]188.483.9
    PANORAMA-NN[15]191.190.7
    SCFN[5]288.889.5
    MVCLN[9]290.388.7
    692.290.6
    MDPCNN[6]387.6
    CNN-VOTE[11]392.391.3
    692.491.9
    RotationNet[12]393.089.0
    FusionNet[7]6093.190.8
    VS-MVCNN[10]8093.590.9
    本文292.489.5
    393.691.0
    694.492.1
    下载: 导出CSV

    表  4  视图被分到附加类的数量统计

    类别总视图数/分到附加类张数比例(%)类别总视图数/分到附加类张数比例(%)
    Bathtub2700/250.9Monitor5400/180.3
    Bed5400/300.6Night_stand4644/471.0
    Chair5400/90.2Sofa5400/150.3
    Desk4644/451.0Table5400/220.4
    Dresser4644/170.4Toilet5400/80.1
    下载: 导出CSV
  • [1] 韩丽, 刘书宁, 徐圣斯, 等. 自适应稀疏编码融合的非刚性三维模型分类算法[J]. 计算机辅助设计与图形学学报, 2019, 31(11): 1898–1907. doi: 10.3724/SP.J.1089.2019.17759

    HAN Li, LIU Shuning, XU Shengsi, et al. Non-rigid 3D model classification algorithm based on adaptive sparse coding fusion[J]. Journal of Computer-Aided Design &Computer Graphics, 2019, 31(11): 1898–1907. doi: 10.3724/SP.J.1089.2019.17759
    [2] 周文, 贾金原. 一种SVM学习框架下的Web3D轻量级模型检索算法[J]. 电子学报, 2019, 47(1): 92–99. doi: 10.3969/j.issn.0372-2112.2019.01.012

    ZHOU Wen and JIA Jinyuan. Web3D lightweight for sketch-based shape retrieval using SVM learning algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 92–99. doi: 10.3969/j.issn.0372-2112.2019.01.012
    [3] 王栋. 面向三维模型检索的多视图特征学习方法研究[D]. [博士论文], 哈尔滨工业大学, 2019: 1–15.

    WANG Dong. Research on multi-view feature learning for 3D model retrieval[D]. [Ph. D. dissertation], Harbin Institute of Technology, 2019: 1–15.
    [4] SU Hang, MAJI S, KALOGERAKIS E, et al. Multi-view convolutional neural networks for 3D shape recognition[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 945–953.
    [5] LIU Anan, GUO Fubin, ZHOU Heyu, et al. Semantic and context information fusion network for view-based 3D model classification and retrieval[J]. IEEE Access, 2020, 8: 155939–155950. doi: 10.1109/ACCESS.2020.3018875
    [6] GAO Zan, XUE Haixin, and WAN Shaohua. Multiple discrimination and pairwise CNN for view-based 3D object retrieval[J]. Neural Networks, 2020, 125: 290–302. doi: 10.1016/j.neunet.2020.02.017
    [7] HEGDE V and ZADEH R. FusionNet: 3D object classification using multiple data representations[EB/OL]. https://arxiv.org/abs/1607.05695, 2016.
    [8] LIU Anan, ZHOU Heyu, LI Mengjie, et al. 3D model retrieval based on multi-view attentional convolutional neural network[J]. Multimedia Tools and Applications, 2020, 79(7-8): 4699–4711. doi: 10.1007/s11042-019-7521-8
    [9] LIANG Qi, WANG Yixin, NIE Weizhi, et al. MVCLN: Multi-view convolutional LSTM network for cross-media 3D shape recognition[J]. IEEE Access, 2020, 8: 139792–139802. doi: 10.1109/ACCESS.2020.3012692
    [10] MA Yanxun, ZHENG Bin, GUO Yulan, et al. Boosting multi-view convolutional neural networks for 3D object recognition via view saliency[C]. The 12th Chinese Conference on Image and Graphics Technologies, Beijing, China, 2017: 199–209.
    [11] 白静, 司庆龙, 秦飞巍. 基于卷积神经网络和投票机制的三维模型分类与检索[J]. 计算机辅助设计与图形学学报, 2019, 31(2): 303–314. doi: 10.3724/SP.J.1089.2019.17160

    BAI Jing, SI Qinglong, and QIN Feiwei. 3D model classification and retrieval based on CNN and voting scheme[J]. Journal of Computer-Aided Design &Computer Graphics, 2019, 31(2): 303–314. doi: 10.3724/SP.J.1089.2019.17160
    [12] KANEZAKI A, MATSUSHITA Y, and NISHIDA Y. RotationNet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 5010–5019.
    [13] SHI Baoguang, BAI Song, ZHOU Zhichao, et al. DeepPano: Deep panoramic representation for 3-D shape recognition[J]. IEEE Signal Processing Letters, 2015, 22(12): 2339–2343. doi: 10.1109/LSP.2015.2480802
    [14] SINHA A, BAI Jing, and RAMANI K. Deep learning 3D shape surfaces using geometry images[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 223–240.
    [15] SFIKAS K, THEOHARIS T, and PRATIKAKIS I. Exploiting the PANORAMA representation for convolutional neural network classification and retrieval[C]. The 10th Eurographics Workshop on 3D Object Retrieval, Lyon, France, 2017: 1–7.
    [16] HAN Zhizhong, SHANG Mingyang, LIU Zhenbao, et al. SeqViews2SeqLabels: Learning 3D global features via aggregating sequential views by RNN with attention[J]. IEEE Transactions on Image Processing, 2019, 28(2): 658–672. doi: 10.1109/TIP.2018.2868426
    [17] WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 3–19.
    [18] WOO S M, LEE S H, YOO J S, et al. Improving color constancy in an ambient light environment using the phong reflection model[J]. IEEE Transactions on Image Processing, 2018, 27(4): 1862–1877. doi: 10.1109/TIP.2017.2785290
    [19] SHILANE P, MIN P, KAZHDAN M, et al. The Princeton shape benchmark[C]. Shape Modeling Applications, 2004, Genova, Italy, 2004: 167–178.
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出版历程
  • 收稿日期:  2021-08-12
  • 修回日期:  2022-03-15
  • 录用日期:  2022-03-31
  • 网络出版日期:  2022-04-10
  • 刊出日期:  2022-11-14

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